Novel Transfer Learning Approaches forImproving Brain Computer Interfaces

Despite several recent advances, most of the electroencephalogram(EEG)-based brain-computer interface (BCI) applications are still limited to the laboratory due to their long calibration time. Due toconsiderable inter-subject/inter-session and intra-session variations, atime-consuming and fatiguing calibration phase is typically conductedat the beginning of each new session to acquire sufficient labelled train-ing data to train the subject-specific BCI model.This thesis focuses on developing reliable machine learning algorithmsand approaches that reduce BCI calibration time while keeping accu-racy in an acceptable range. Calibration time could be reduced viatransfer learning approaches where data from other sessions or sub-jects are mined and used to compensate for the lack of labelled datafrom the current user or session. In BCI, transfer learning can beapplied on either raw EEG, feature or classification domains.In this thesis, firstly, a novel weighted transfer learning approach isproposed in the classification domain to improve the MI-based BCIperformance when only few subject-specific trials are available fortraining.Transfer learning techniques should be applied in a different domainbefore the classification domain to improve the classification accuracyfor subjects whom their subject-specific features for different classesare not separable. Thus, secondly, this thesis proposes a novel regu-larized common spatial patterns framework based on dynamic timewarping and transfer learning (DTW-R-CSP) in raw EEG and featuredomains.In previous transfer learning approaches, it is hypothesised that thereare enough labelled trials available from the previous subjects or ses-sions. However, in the case when there are no labelled trials available from other subjects or sessions, domain adaptation transfer learningcould potentially mitigate problems of having small training size byreducing variations between the testing and training trials. Thus, todeal with non-stationarity between training and testing trials, a novelensemble adaptation framework with temporal alignment is proposed

Tiny ML in Microcontroller to Classify EEG Signal into Three States

This thesis investigates how to implement an own-built neural network for electroencephalography signals classification on an STM32L475VG microcontroller unit. The original dataset is analyzed and processed to better understand the brain signals. There is a comparison between three machine learning algorithms (linear support vector machine, extreme gradient boosting, and deep neural network) in three testing paradigms: specific-subject, all-subject, and adaptable to select the most appropriate approach for deploying on the microcontroller. The implementation procedure with detailed notation is presented, and the inference is also performed to feasible observation. Finally, possible improvement solutions are proposed within a clear demonstration.This thesis investigates how to implement an own-built neural network for electroencephalography signals classification on an STM32L475VG microcontroller unit. The original dataset is analyzed and processed to better understand the brain signals. There is a comparison between three machine learning algorithms (linear support vector machine, extreme gradient boosting, and deep neural network) in three testing paradigms: specific-subject, all-subject, and adaptable to select the most appropriate approach for deploying on the microcontroller. The implementation procedure with detailed notation is presented, and the inference is also performed to feasible observation. Finally, possible improvement solutions are proposed within a clear demonstration

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Simulation and implementation of novel deep learning hardware architectures for resource constrained devices

Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic